An abstraction system for generating a standard customer profile in a data processing system has a processing device and a memory. The abstraction system may receive customer data from a computing device over a network, perform unsupervised learning on the customer data to produce a plurality of clusters of customers with a plurality of features in common, and determine that a cluster represents a standard customer, and store a plurality of standard customer profiles based on the determined standard customers, wherein the standard customer profiles comprise a plurality of data distributions for the plurality of features in common. The abstraction system additionally provides the standard customer profiles and the additional standard customer profiles to a cognitive system for generating synthetic transaction data.
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2. The method of claim 1, wherein the information for the plurality of customers comprises identifying information and transaction information.
3. The method of claim 1, further comprising filtering the customer data prior to performing unsupervised learning.
The invention relates to a method for processing customer data using unsupervised learning techniques to identify patterns or insights. The method addresses the challenge of extracting meaningful information from large, unstructured customer datasets without relying on pre-labeled training data. The core process involves applying unsupervised learning algorithms to analyze the customer data, which may include transaction histories, behavioral metrics, or demographic information, to detect clusters, anomalies, or other significant patterns. Before performing the unsupervised learning, the method includes a preprocessing step to filter the customer data. This filtering step removes irrelevant, noisy, or redundant information to improve the quality and efficiency of the subsequent analysis. The filtering may involve techniques such as data normalization, outlier removal, or feature selection to ensure the data is optimized for the unsupervised learning algorithm. The filtered data is then processed by the unsupervised learning algorithm, which identifies hidden structures or groupings within the dataset. The results can be used for applications such as customer segmentation, fraud detection, or personalized marketing strategies. The method enhances the accuracy and reliability of insights derived from customer data by ensuring the input data is clean and relevant before analysis.
4. The method of claim 3, wherein the filtering comprises an RFM analysis to group customers.
This invention relates to customer segmentation and analysis in a business or marketing context, specifically addressing the challenge of efficiently categorizing customers to optimize engagement and resource allocation. The method involves filtering customer data using RFM (Recency, Frequency, Monetary) analysis to group customers into distinct segments based on their purchasing behavior. RFM analysis evaluates three key metrics: recency (how recently a customer made a purchase), frequency (how often they purchase), and monetary value (how much they spend). By applying this analysis, businesses can identify high-value customers, at-risk customers, or inactive customers, enabling targeted marketing strategies and improved customer retention. The filtering process may also include additional criteria beyond RFM, such as demographic or behavioral data, to refine segmentation further. The segmented customer groups can then be used to tailor communications, promotions, or loyalty programs, enhancing customer satisfaction and business performance. This approach helps businesses allocate resources more effectively by focusing on the most profitable customer segments while addressing potential churn risks. The method is particularly useful in e-commerce, retail, and subscription-based services where customer behavior data is readily available.
5. The method of claim 1, wherein the performing unsupervised learning comprises clustering customers based on a feature in common and repeating unsupervised learning to form sub-clusters of customers based on the plurality of features in common.
This invention relates to customer segmentation using unsupervised learning techniques. The method addresses the challenge of identifying distinct customer groups within a dataset without relying on pre-labeled data. The process begins by clustering customers based on a single shared feature, such as purchasing behavior or demographic attributes. After forming initial clusters, the method iteratively applies unsupervised learning to refine these clusters by incorporating additional shared features. This iterative clustering creates sub-clusters, allowing for more granular segmentation of customers. The technique enables businesses to uncover hidden patterns in customer data, improving targeted marketing, personalized recommendations, and customer relationship management. By progressively refining clusters through multiple iterations, the method ensures that customer segments are based on a comprehensive analysis of multiple features, leading to more accurate and actionable insights. The approach is particularly useful in large datasets where manual segmentation would be impractical. The iterative clustering process enhances the precision of customer segmentation, making it adaptable to evolving customer behaviors and market conditions.
6. The method of claim 1, wherein determining that a cluster represents a standard customer comprises applying one or more rules.
7. The method of claim 6, wherein the one or more rules comprise a size determination indicating a minimum or maximum number of customers in a cluster that is determined to be a standard customer.
9. The system of claim 8, wherein the information for the plurality of customers comprises identifying information and transaction information.
11. The system of claim 10, wherein the filtering comprises an RFM analysis to group customers.
A system for customer segmentation and analysis in a business or marketing context. The system addresses the challenge of efficiently categorizing customers to optimize marketing strategies, improve customer retention, and enhance targeted engagement. The system includes a data processing module that collects and processes customer data, such as purchase history, interaction frequency, and monetary value. A segmentation module applies a Recency, Frequency, Monetary (RFM) analysis to group customers into distinct segments based on their behavior. The RFM analysis evaluates how recently a customer made a purchase (recency), how often they make purchases (frequency), and how much they spend (monetary value). These segments help businesses identify high-value customers, at-risk customers, and inactive customers, enabling tailored marketing efforts. The system may also include a visualization module to display the segmented customer groups and their characteristics, allowing for data-driven decision-making. The RFM analysis enhances traditional segmentation methods by providing a more nuanced understanding of customer behavior, leading to more effective marketing campaigns and improved customer lifetime value.
12. The system of claim 8, wherein the performing unsupervised learning comprises clustering customers based on a feature in common and repeating unsupervised learning to form sub-clusters of customers based on the plurality of features in common.
13. The system of claim 8, wherein determining that a cluster represents a standard customer comprises applying one or more rules.
The system is designed for customer segmentation in a business or marketing context, where the challenge is accurately identifying and categorizing different types of customers to improve targeting and service. The system groups customer data into clusters based on shared characteristics, such as purchase history, behavior, or demographics. To determine whether a cluster represents a "standard customer," the system applies predefined rules. These rules may include thresholds for purchase frequency, transaction value, or engagement metrics. For example, a standard customer might be defined as one who makes purchases within a certain frequency range and spends within a specified value range. The rules ensure that the system can distinguish standard customers from other categories, such as high-value or inactive customers. This classification helps businesses tailor their strategies, such as marketing campaigns or customer service approaches, to better meet the needs of different customer segments. The system may also use additional data, such as customer feedback or interaction history, to refine the rules and improve accuracy over time. The goal is to automate the identification of standard customers, reducing manual effort and enhancing decision-making efficiency.
14. The system of claim 13, wherein the one or more rules comprise a size determination indicating a minimum or maximum number of customers in a cluster that is determined to be a standard customer.
The invention relates to a system for analyzing customer data to identify standard customers within clusters of customer data. The system addresses the challenge of accurately categorizing customers into meaningful groups based on predefined criteria, particularly focusing on cluster size to ensure statistical relevance or business applicability. The system includes a data processing module that receives customer data and applies clustering algorithms to group customers into clusters. A rule-based evaluation module then assesses these clusters using predefined rules, including a size determination rule that specifies a minimum or maximum number of customers required for a cluster to be classified as a standard customer group. This ensures that clusters are neither too small to be statistically insignificant nor too large to lose granularity. The system may also include a reporting module to output the results, such as identifying clusters that meet the size criteria and flagging those that do not. The invention improves customer segmentation by enforcing size constraints, leading to more reliable and actionable insights for businesses.
16. The method of claim 15, wherein the information for the plurality of customers comprises identifying information and transaction information.
17. The method of claim 15, further comprising filtering the customer data prior to performing unsupervised learning.
This invention relates to data processing systems that analyze customer data using unsupervised learning techniques. The problem addressed is the presence of noise, irrelevant information, or inconsistencies in raw customer data, which can degrade the accuracy and reliability of unsupervised learning models. The solution involves preprocessing the customer data by applying filtering techniques before performing unsupervised learning. Filtering may include removing outliers, normalizing values, handling missing data, or selecting relevant features to improve the quality of the input data. The filtered data is then processed using unsupervised learning algorithms, such as clustering or dimensionality reduction, to extract meaningful patterns or insights without prior labeling. This preprocessing step ensures that the unsupervised learning model operates on cleaner, more representative data, leading to more accurate and interpretable results. The invention is applicable in fields like customer segmentation, fraud detection, and personalized marketing, where high-quality data is critical for effective analysis.
18. The method of claim 17, wherein the filtering comprises an RFM analysis to group customers.
This invention relates to customer segmentation and analysis in a business or marketing context, specifically addressing the challenge of efficiently categorizing customers to optimize engagement strategies. The method involves filtering customer data to group individuals based on their behavior, value, and engagement patterns. A key aspect of the filtering process is the use of RFM (Recency, Frequency, Monetary) analysis, a widely used technique in customer segmentation that evaluates how recently a customer made a purchase (recency), how often they make purchases (frequency), and how much they spend (monetary value). By applying RFM analysis, businesses can identify high-value customers, at-risk customers, or those likely to respond to targeted marketing efforts. The method may also include additional filtering steps, such as clustering or predictive modeling, to further refine customer groups. The goal is to enable businesses to tailor their marketing, sales, or customer service strategies based on these segmented groups, improving customer retention and revenue generation. The invention is particularly useful in e-commerce, retail, and subscription-based services where customer behavior data is abundant and actionable insights are valuable.
19. The method of claim 15, wherein the performing unsupervised learning comprises clustering customers based on a feature in common and repeating unsupervised learning to form clusters of customers based on the plurality of features in common.
20. The method of claim 15, wherein determining that a cluster represents a standard customer comprises applying one or more rules.
21. The method of claim 20, wherein the one or more rules comprise a size determination indicating a minimum or maximum number of customers in a cluster that is determined to be a standard customer.
This invention relates to customer segmentation in data analysis, specifically addressing the challenge of accurately identifying and grouping standard customers within a dataset. The method involves applying one or more rules to determine whether a customer belongs to a cluster classified as a standard customer. A key aspect of these rules is a size determination, which specifies either a minimum or maximum number of customers that must be present in a cluster for it to be considered a standard customer cluster. This ensures that the segmentation process adheres to predefined thresholds, preventing overly small or excessively large clusters that could skew analysis results. The method may also include additional rules, such as criteria based on customer behavior, demographics, or transaction history, to further refine the clustering process. By enforcing these constraints, the system improves the reliability and consistency of customer segmentation, enabling businesses to make more informed decisions based on accurate data groupings. The approach is particularly useful in industries like retail, finance, and marketing, where understanding customer behavior is critical for targeted strategies.
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November 5, 2019
October 4, 2022
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